Abstract P38: Pooled Analysis of Long and Short Term Outcomes After Subarachnoid Hemorrhage - International Stroke Outcomes Study (INSTRUCT)
Bibliographic record
Abstract
Background: Outcomes after subarachnoid hemorrhage (SAH) have been rarely examined in large cohorts. Methods: This is an extension of the International Stroke Outcomes Study (INSTRUCT) pooling 13 ‘ideal’ stroke incidence studies (n=657 with SAH from 1993-2017, median age 56 years; 46% men). The primary outcomes were mortality and functional outcome (mRS score 3-5). Harmonized study factors included age, sex, behaviors (current smoking, alcohol intake), comorbidities (history of hypertension, ischemic heart disease, atrial fibrillation), stroke severity (e.g. NIHSS score) and year of stroke. In the pooled dataset, we estimated predictors of mortality using Poisson regression, to estimate incidence rate ratio (IRR) at 1 month (11 studies), 1 year (12 studies) and 5 years (7 studies). Generalized equation estimates in the log-binomial family were used to calculate risk ratios (RRs) for predictors of poor functional outcome at 1 month (5 studies) and 1 year (8 studies). Results: Mortality was 33% at 1 month, 43% at 1 year, and 47% at 5 years (Fig 1). Poor functional outcome was 25% at 1 month and 15% at 1 year (Fig 1). In multivariable analysis, age and stroke severity were associated with mortality at all time points, together with current smoking at 1 and 5 years, and history of hypertension at 5 years (Fig 2). Poor functional outcome was predicted by age (RR 1.03; 95% CI 1.01-1.04) at 1 month and by age (RR 1.04; 95% CI 1.00-1.08) and stroke severity (RR 1.94; 95% CI 1.02-2.87) at 1 year. Conclusion: Risk factors that predict SAH incidence including hypertension and smoking make outcomes worse. Better management of older patients and those with severe strokes could improve outcomes after SAH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".